Parameters optimization of central pattern generators for hexapod robot based on multi-objective genetic algorithm

Binrui Wang,Jianbo Sun,Yanfeng Gao,Xiaohong Cui

doi:10.1177/17298814211044934

Binrui Wang, Jianbo Sun + Show 2 more

Open Access

https://doi.org/10.1177/17298814211044934

Copy DOI

Abstract

In this article, a network of central pattern generators is used for the motion planning of a hexapod robot. There are many parameters in the planning network, which determine the motion performance of the hexapod robot. On the other hand, the network is a highly nonlinear coupling network, which is difficult to obtain optimal parameters by an analytical method. Optimizing these parameters to make the robot walk well is a multi-objective optimization process. There is a certain mutual exclusion relationship among the targets. To find a well-performing network as soon as possible, a multi-objective genetic algorithm is used for the process of parameter tuning. The hexapod robot simulation model is performed in Webots, and the motion performance parameters of the robot are obtained through built-in sensors and are also considered as mean values of the optimization algorithm. The optimization algorithm is written and run with MATLAB. Finally, the optimization algorithm and simulation results are proven by an experiment.

Highlights

The hexapod robot is one of the most important legged robots
The velocity is measured by a distance sensor between the experimental platform and the front board in the process of moving forward, and the distance is divided by time
The velocity used in the experiment is based on the displacement of the experimental platform measured by the displacement sensor, which is the difference of displacement

Summary

Introduction

The hexapod robot is one of the most important legged robots. Hexapod robots have better stability and flexibility compared with biped and quadruped robots.[1,2,3] Even with a failed leg, the hexapod robot can go on walking.[4]. The optimization of the robot’s target values (velocity, setback, bump) is achieved by optimizing the parameters of the CPG network oscillator. The hip joint of the robot needs to provide a larger rotation angle In the process of optimizing the minimum setback value, the nonlinear relationship between the foot motion and the motion of the hip joint supporting this foot limits the movement velocity of the hip joint.

Introduction of experimental prototype

Findings

Conclusion